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Subject-tailored parallel transmission pulses for ultra-high fields body applications are typically calculated based on subject-specific B + 1 -maps of all transmit channels, which require lengthy adjustment times. This study investigates the feasibility of using deep learning to estimate complex, channel-wise, relative 2D B + 1 -maps from a single gradient echo localizer to overcome long calibration times.Methods: 126 channel-wise, complex, relative 2D B + 1 -maps of the human heart from 44 subjects were acquired at 7T using a Cartesian, cardiac gradient-echo sequence obtained under breath-hold to create a library for network training and cross-validation. The deep learning predicted maps were qualitatively compared to the ground truth. Phase-only B + 1 -shimming was subsequently performed on the estimated B + 1 -maps for a region of interest covering the heart. The proposed network was applied at 7T to 3 unseen test subjects. Results: The deep learning-based B +1 -maps, derived in approximately 0.2 seconds, match the ground truth for the magnitude and phase. The static, phase-only pulse design performs best when maximizing the mean transmission efficiency. In-vivo application of the proposed network to unseen subjects demonstrates the feasibility of this approach: the network yields predicted B + 1 -maps comparable to the acquired ground truth and anatomical scans reflect the resulting B + 1 -pattern using the deep learning-based maps. Conclusion:The feasibility of estimating 2D relative B + 1 -maps from initial localizer scans of the human heart at 7T using deep learning is successfully demonstrated. Because the technique requires only sub-seconds to derive channel-wise B + 1 -maps, it offers high potential for advancing clinical body imaging at ultra-high fields.

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This paper demonstrates a refined approach to solving dynamic optimization problems for underactuated marine surface vessels. To this end the differential flatness of a mathematical model assuming full actuation is exploited to derive an efficient representation of a finite dimensional nonlinear programming problem, which in turn is constrained to apply to the underactuated case. It is illustrated how the properties of the flat output can be employed for the generation of an initial guess to be used in the optimization algorithm in the presence of static and dynamic obstacles. As an example energy optimal point to point trajectory planning for a nonlinear 3 degrees of freedom dynamic model of an underactuated surface vessel is undertaken. Input constraints, both in rate and magnitude as well as state constraints due to convex and non-convex obstacles in the area of operation are considered and simulation results for a challenging scenario are reported. Furthermore, an extension to a trajectory tracking controller using model predictive control is made where the benefits of the flatness based direct method allow to introduce nonuniform sample times that help to realize long prediction horizons while maintaining short term accuracy and real time capability. This is also verified in simulation where additional disturbances in the form of environmental disturbances, dynamic obstacles and parameter mismatch are introduced.

At 7T, power restrictions are a major limitation to accurately map the absolute transmit magnetic field (B1+) in the body. To overcome this, we investigate an absolute B1+ mapping method with low RF power using magnetic resonance fingerprinting (MRF). Measurements are done in a phantom at 3T and in-vivo in the liver at 7T. Resulting maps are compared to the actual flip angle (AFI) method. The obtained results show good agreement between the two methods, while the MRF approach seems to perform better in regions of low B1+ amplitude. Motion robustness introduced by a radial acquisition scheme enables free-breathing measurements.